Efficient gradient descent algorithm with anderson acceleration for separable nonlinear models

Separable nonlinear models are pervasively employed in diverse disciplines, such as system identification, signal analysis, electrical engineering, and machine learning. Identifying these models inherently poses a non-convex optimization challenge. While gradient descent (GD) is a commonly adopted m...

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Bibliographic Details
Published in:	Nonlinear dynamics Vol. 113; no. 10; pp. 11371 - 11387
Main Authors:	Chen, Guang-Yong, Lin, Xin, Xue, Peng, Gan, Min
Format:	Journal Article
Language:	English
Published:	Dordrecht Springer Netherlands 01.05.2025 Springer Nature B.V
Subjects:	Algorithms Applications of Nonlinear Dynamics and Chaos Theory Classical Mechanics Control Convergence Convexity Dynamical Systems Hessian matrices Machine learning Newton methods Optimization Physics Physics and Astronomy Signal analysis Statistical Physics and Dynamical Systems System identification Vibration Anderson acceleration Robust parameter estimation Hierarchical identification algorithm Separable nonlinear problem
ISSN:	0924-090X, 1573-269X
Online Access:	Get full text
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